:j0_k_norm => (log10 => L"Log Normalized Current Density ($J_A$)")
Code
begin dir ="../data/05_reporting" j_events_low_fit =load() j_events_tau_20_fit =load(tau =20) j_events_high_fit =load(ts =0.12) j_events_low_der =load(method="derivative") j_events_high_der =load(ts =0.12, method="derivative") w_events =load("$dir/events.Wind.fit.ts_0.09s_tau_60s.arrow")# add a label column to the dataframes j_events_low_fit[!, :label] .="1 Hz (fit)" j_events_high_fit[!, :label] .="8 Hz (fit)" j_events_tau_20_fit[!, :label] .="1 Hz, 20s (fit)" j_events_low_der[!, :label] .="1 Hz (derivative)" j_events_high_der[!, :label] .="8 Hz (derivative)"# filter high time resolution events j_events_high_fit =@chain j_events_high_fit beginfilter(:len =>>(240), _)end j_events_der =vcat(j_events_low_der, j_events_high_der, cols=:intersect)# combine the dataframes j_events =reduce(vcat, [j_events_low_fit, j_events_high_fit, j_events_tau_20_fit]) j_events =@chain j_events beginfilter(:"fit.stat.rsquared"=>>(0.95), _)endprintln("Number of events: ", size(j_events, 1))end
┌ Warning: automatically converting Arrow.Timestamp with precision = NANOSECOND to `Dates.DateTime` which only supports millisecond precision; conversion may be lossy; to avoid converting, pass `Arrow.Table(source; convert=false)
└ @ Arrow /Users/zijin/.julia/packages/Arrow/Y6R1E/src/eltypes.jl:273
# groupby r and describe the data for each group # j_events |> @groupby(_.r) |> @map({r=key(_), j0_k=describe(_.j0_k), L_k=describe(_.L_k)})@chain j_events begingroupby(:r)combine(:plasma_density => mean, :ion_inertial_length => mean, :b_mag => mean) end